Instructions to use desaxce/Qwen2.5-Coder-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use desaxce/Qwen2.5-Coder-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="desaxce/Qwen2.5-Coder-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("desaxce/Qwen2.5-Coder-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("desaxce/Qwen2.5-Coder-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use desaxce/Qwen2.5-Coder-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "desaxce/Qwen2.5-Coder-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "desaxce/Qwen2.5-Coder-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/desaxce/Qwen2.5-Coder-7B-Instruct

SGLang

How to use desaxce/Qwen2.5-Coder-7B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "desaxce/Qwen2.5-Coder-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "desaxce/Qwen2.5-Coder-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "desaxce/Qwen2.5-Coder-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "desaxce/Qwen2.5-Coder-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use desaxce/Qwen2.5-Coder-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/desaxce/Qwen2.5-Coder-7B-Instruct
```

Qwen2.5-Coder-7B-Instruct / handler.py

desaxce

Create handler.py

c8dd61d verified over 1 year ago

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1.04 kB

	from typing import Any, Dict, List

	import torch
	from transformers import AutoTokenizer, Qwen2ForCausalLM, pipeline

	dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16


	class EndpointHandler:
	def __init__(self, path=""):
	# load the model
	self.tokenizer = AutoTokenizer.from_pretrained(path)
	model = Qwen2ForCausalLM.from_pretrained(
	path, device_map="auto", torch_dtype=dtype
	)
	# create inference pipeline
	self.pipeline = pipeline("text-generation", model=model, tokenizer=self.tokenizer)

	def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
	inputs = data.pop("inputs", data)
	parameters = data.pop("parameters", None)

	# pass inputs with all kwargs in data
	if parameters is not None:
	prediction = self.pipeline(inputs, tokenizer=self.tokenizer, **parameters)
	else:
	prediction = self.pipeline(inputs)
	# postprocess the prediction
	return prediction